Contourlet-S Texture Image Retrieval System

Abstract:

Contourlet transform is better in direction information representation than wavelet transform which has been studied in retrieval systems and has been shown that it is superior to wavelet ones at retrieval rate. In order to improve the retrieval rate further, an anti-aliasing contourlet-S transform based texture image retrieval system was proposed. In this system, the contourlet transform was constructed by anti-aliasing non-subsampled Laplacian pyramid cascaded by critical sub-sampled directional filter banks, sub-bands energy and standard deviations in contourlet domain are cascaded to form feature vectors, and the similarity metric used here is Canberra distance. Experimental results show that contourlet-S transform based image retrieval system is superior to those of the original contourlet transform, and non-subsampled contourlet system under the same system structure with almost same dimension of feature vectors, retrieval time and memory needed; and contourlet decomposition structure parameters can make significant effects on retrieval rates, especially scale number. To improve the retrieval rate of this system, kurtosis in each sub-band coefficients can be incorporated in features at the cost of some higher dimension of feature vectors.

• Periodical: Advanced Materials Research (Volumes 433-440)
• Pages: 3408-3412
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.433-440.3408
• Online since: January 2012
• Authors: Jian Zhong Ma, Xin Wu Chen, Li Juan Zhong
• Keywords: Content-Base Image Retrieval, Contourlet-S Transform, Retrieval Rate, Retrieval System, Texture Image

References

[1] Smeulders A, Worring M, Santini S, etc. Content- based image retrieval at the end of the early years, IEEE Trans. Pattern Recognit. Machine intell., Vol 22, No. 12, p.1349–1380, Dec, (2000).

DOI: 10.1109/34.895972

[2] Minh N Do, Martin Vetterli. M, Wavelet-based texture retrieval using Generalized Gaussian density and kullback-leibler distance, IEEE transactions on image processing, Vol 11, No. 2, pp.146-158, Feb. (2002).

DOI: 10.1109/83.982822

[3] Laine A, Fan J, Texture classification by wavelet packet signatures, IEEE trans. pattern recognit. machine intell., Vol 15, p.1186–1191, Nov. (1993).

DOI: 10.1109/34.244679

[4] Chang T, Kuo C, Texture analysis and classification with tree-structure wavelet transform, IEEE trans. on image processing, Vol 2, p.429–441, Oct. (1993).

DOI: 10.1109/83.242353

[5] Smith J R, Chang S F, Transform features for texture classification and discrimination in large image databases, Proceedings of IEEE Int Conf. on Image Processing, Texas, pp.407-411, November (1994).

DOI: 10.1109/icip.1994.413817

[6] Do, M N, Vetterli M. Contourlets: a directional multiresolution image representation, International Conference on Image Processing. New York, pp.357-360, September, (2002).

DOI: 10.1109/icip.2002.1038034

[7] Cunha D, Zhou J, Do M N, The nonsubsampled contourlet transform: theory, design, and applications, IEEE transactions on image processing, Vol 15, p.3089 – 3101, Oct. (2006).

DOI: 10.1109/tip.2006.877507

[8] Lu Y, Do M N. A new contourlet transform with sharp frequency localization, Proceeding of IEEE International Conference on Image Processing, Atlanta, pp.8-11, Oct. (2006).

DOI: 10.1109/icip.2006.312657

[9] Qimin Cheng and Guangxi Zhu. Contourlet spectral histogram for texture retrieval of remotely sensed imagery,. Proceeding of SPIE on Remote Sensing and GIS Data Processing and Other Applications, Yichang, pp. 74981R-74981R-6, October, (2009).

DOI: 10.1117/12.833964